Article: Monetizing your models with machine learning solutions, by Stu Bailey, Contributor, InfoWorld
Article: Monetizing your models with machine learning solutions, by Stu Bailey, Contributor, InfoWorld
Article: Key Steps to Model Creation: Data Cleaning & Data Exploration, by Stu Bailey, Contributor, InfoWorld
Programming has redefined itself over the years from simply writing code to now finding effective solutions to any problem related to software development, algorithm, analytics, etc. These solutions have required the help of various software tools that are not always compatible with one another.
Last Wednesday, Open Data Group had the opportunity to co-host a data science meet-up with DataScope, which manages the Data Science Chicago Meet-Up. We thoroughly enjoyed the experience and appreciate all the folks who came out for discussion and pizza. Bob Grossman, Open Data’s founder and Chief Data Scientist, introduced the concept of AnalyticOps (read CTO Stu Bailey’s posts on the same topic here) and the emerging core competency of deploying models. Bob was joined by Robert Nendorf from Allstate, who shared his views on a similar topic: DevOps for Data Science.
Well, it took four parts to get to this point, but we’ve used our time to discuss some of the abstractions that are required to understand the idea referred to as “AnalyticOps”. Our journey started with the abstract concept of “what is an analytic” while the second covered the operative concept of “deploying” the analytic with an analytic engine or deployment server.
